This proposal originates from discussions between scientists from different scientific fields: Drosophila aging physiology, information theory, statistics, bioengineering and systems biology. Biological dysfunction in aging is complex and global, and therapeutic interventions on multiple targets are likely to be necessary. When we screen drugs, or combinations of them, the efficiency of transmission of information is important, not simply the meaning of the signal. Therefore some aspects of information theory are expected to be relevant, integrated with an in depth knowledge of the biological system and high-throughput techniques to interrogate it on a large scale. The phenotyping techniques we have developed measure cardiac function and exercise capacity in Drosophila. We wish to test two algorithms that help in the search for the optimal combinations of drug when using a large number of compounds. The algorithms are derived from information theory but we adapted them to combinations of molecular interventions, with the output obtained in our Drosophila biological system. These algorithms could be extended to other models and interventions used in aging and in complex diseases. This project will provide methods and suggest interventions that might alleviate the declines in cardiac function and exercise capacity with age. These age-related changes have a major effect on the quality of life of older individuals.

Agency
National Institute of Health (NIH)
Institute
National Institute on Aging (NIA)
Type
Exploratory/Developmental Grants (R21)
Project #
1R21AG030685-01A1
Application #
7387640
Study Section
Modeling and Analysis of Biological Systems Study Section (MABS)
Program Officer
Kohanski, Ronald A
Project Start
2009-08-01
Project End
2011-07-31
Budget Start
2009-08-01
Budget End
2010-07-31
Support Year
1
Fiscal Year
2009
Total Cost
$195,775
Indirect Cost
Name
Sanford-Burnham Medical Research Institute
Department
Type
DUNS #
020520466
City
La Jolla
State
CA
Country
United States
Zip Code
92037
Feala, Jacob D; Cortes, Jorge; Duxbury, Phillip M et al. (2012) Statistical properties and robustness of biological controller-target networks. PLoS One 7:e29374
Tiziani, Stefano; Kang, Yunyi; Choi, Janet S et al. (2011) Metabolomic high-content nuclear magnetic resonance-based drug screening of a kinase inhibitor library. Nat Commun 2:545
Feala, Jacob D; Coquin, Laurence; Zhou, Dan et al. (2009) Metabolism as means for hypoxia adaptation: metabolic profiling and flux balance analysis. BMC Syst Biol 3:91